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Research Article

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

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  • @ARTICLE{10.4108/eetpht.10.5267,
        author={S Roobini and M S Kavitha and S Karthik},
        title={Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={3},
        keywords={Alzheimer's Disease, AD, Classification, Conventional Neural Network, CNN, Deep Learning, R-CNN, Faster R-CNN, Magnetic Resonance Imaging, MRI},
        doi={10.4108/eetpht.10.5267}
    }
    
  • S Roobini
    M S Kavitha
    S Karthik
    Year: 2024
    Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5267
S Roobini1,*, M S Kavitha1, S Karthik1
  • 1: SNS College of Technology
*Contact email: srruby13@gmail.com

Abstract

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD). OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models. METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN. RESULTS:  On the confusion matrix, R-CNN performed the best. CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

Keywords
Alzheimer's Disease, AD, Classification, Conventional Neural Network, CNN, Deep Learning, R-CNN, Faster R-CNN, Magnetic Resonance Imaging, MRI
Received
2023-12-06
Accepted
2024-02-23
Published
2024-03-01
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5267

Copyright © 2024 S. Roobini et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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